Gradient-based optimization algorithms can be studied from the perspective of limiting
ordinary differential equations (ODEs). Motivated by the fact that existing ODEs do not …

In a real Hilbert space H, in order to develop fast optimization methods, we analyze the
asymptotic behavior, as time t tends to infinity, of a large class of autonomous dissipative …

CMS/CAIMS Books in Mathematics is a collection of monographs and graduatelevel
textbooks published in cooperation jointly with the Canadian Mathematical Society-Societé …

In a Hilbert space setting, for convex optimization, we analyze the convergence rate of a
class of first-order algorithms involving inertial features. They can be interpreted as discrete …

First-order methods are often analyzed via their continuous-time models, where their worst-
case convergence properties are usually approached via Lyapunov functions. In this work …

In a Hilbert space setting, for convex optimization, we show the convergence of the iterates
to optimal solutions for a class of accelerated first-order algorithms. They can be interpreted …

The aim is to optimise the procedures and reduce the workload of human resource
management (HRM), thereby increasing the working efficiency and improving system …

We investigate the asymptotic properties of the trajectories generated by a second-order
dynamical system with Hessian driven damping and a Tikhonov regularization term in …

We analyze continuous-time models of accelerated gradient methods through deriving
conservation laws in dilated coordinate systems. Namely, instead of analyzing the dynamics …

In this paper, we propose in a Hilbertian setting a second-order time-continuous dynamic
system with fast convergence guarantees to solve structured convex minimization problems …